Collaborative Research: An Application Driven I/O Optimization Approach for PetaScale Systems and Scientific Discoveries

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This research focuses on developing scalable parallel file access methods for multi-scale problem domain decompositions, such as the one presented in Adaptive Mesh Refinement (AMR) based algorithms. Existing parallel I/O methods concentrate on optimizing the process collaboration under a fairly evenly-distributed request pattern. However, they are not suitable for data structures in AMR, because the underlying data distribution is highly irregular and dynamic. Process synchronization in the existing parallel I/O methods can penalize the I/O parallelism if the process collaboration is not carefully coordinated. This research addresses such synchronization issue by developing scalable solutions in the Parallel netCDF library (PnetCDF), particularly to address AMR structured data and its I/O patterns. PnetCDF is a popular I/O library used by many computer simulation communities. A scalable solution for storing and accessing AMR data in parallel is considered a challenging task. This research will design a process-group based parallel I/O approach to eliminate unrelated processes and thus avoid possible I/O serialization. In addition, a new metadata representation will also be developed in pnetCDF for conserving tree-structured AMR data relationship in a portable form.